Add Skill: langgraph-explainable-agent

Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git
Score: 1.0

skills/conditional-request-review/SKILL.md

@@ -1,52 +0,0 @@
----
-name: conditional-request-review
-version: 1.0.0
-description: Capture a user request, classify it via branching, and produce an approval
-  or rejection result.
-inputs:
-- request
-steps:
-- 'Start: input agent prompts user for request and stores it in state field ''request'''
-- 'Classify: branching agent evaluates ''request'' and routes to Approve on success
-  or Reject on failure, storing ''decision'''
-- 'Approve: default agent formats approval message using ''request'' and stores it
-  in ''result'''
-- 'Reject: default agent formats rejection message using ''request'' and stores it
-  in ''result'''
-outputs:
-- result (string message indicating approval or rejection)
-tags: []
-metadata:
-  source_repo: https://github.com/jwwelbor/AgentMap.git
-  extracted_at: ''
-  confidence: 0.85
----
-
-# conditional-request-review
-
-Capture a user request, classify it via branching, and produce an approval or rejection result.
-
-## Steps
-
-1. Start: input agent prompts user for request and stores it in state field 'request'
-2. Classify: branching agent evaluates 'request' and routes to Approve on success or Reject on failure, storing 'decision'
-3. Approve: default agent formats approval message using 'request' and stores it in 'result'
-4. Reject: default agent formats rejection message using 'request' and stores it in 'result'
-
-## Inputs
-
-- request
-
-## Outputs
-
-- result (string message indicating approval or rejection)
-
-## Failure Modes
-
-- If branching classification fails, workflow routes to Reject (on_failure)
-- If input not provided, workflow may hang or error depending on runtime
-
-## Source
-
-Extracted from: [https://github.com/jwwelbor/AgentMap.git](https://github.com/jwwelbor/AgentMap.git)
-Confidence: 0.85

skills/conditional-request-review/commands.md

@@ -1,6 +0,0 @@
-# Commands: conditional-request-review
-
-## Available Commands
-
-- `/skill conditional-request-review` — Load this skill
-- `/run conditional-request-review` — Execute workflow

skills/conditional-request-review/examples.md

@@ -1,10 +0,0 @@
-# Examples: conditional-request-review
-
-## Usage Example
-
-```python
-# How to use this skill
-# Inputs: request
-# Process: Start: input agent prompts user for request and stores it in state field 'request' → Classify: branching agent evaluates 'request' and routes to Approve on success or Reject on failure, storing 'decision' → Approve: default agent formats approval message using 'request' and stores it in 'result'
-# Outputs: result (string message indicating approval or rejection)
-```

skills/conditional-request-review/metadata.json

@@ -1,25 +0,0 @@
-{
-  "name": "conditional-request-review",
-  "version": "1.0.0",
-  "goal": "Capture a user request, classify it via branching, and produce an approval or rejection result.",
-  "inputs": [
-    "request"
-  ],
-  "steps": [
-    "Start: input agent prompts user for request and stores it in state field 'request'",
-    "Classify: branching agent evaluates 'request' and routes to Approve on success or Reject on failure, storing 'decision'",
-    "Approve: default agent formats approval message using 'request' and stores it in 'result'",
-    "Reject: default agent formats rejection message using 'request' and stores it in 'result'"
-  ],
-  "outputs": [
-    "result (string message indicating approval or rejection)"
-  ],
-  "failure_modes": [
-    "If branching classification fails, workflow routes to Reject (on_failure)",
-    "If input not provided, workflow may hang or error depending on runtime"
-  ],
-  "confidence": 0.85,
-  "explanation": "Workflow extracted from AgentMap README example 'ReviewFlow' CSV. It is a generic conditional routing pattern usable for any binary decision process.",
-  "source_repo": "https://github.com/jwwelbor/AgentMap.git",
-  "score": 1.0
-}

skills/langgraph-explainable-agent/SKILL.md

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+---
+name: langgraph-explainable-agent
+version: 1.0.0
+description: Orchestrate an AI agent workflow that provides explainable answers with
+  citations using knowledge graph retrieval and permission-aware search
+inputs:
+- user_query - text input from the user
+- context_documentation - pre-indexed documents for retrieval
+- knowledge_graph - graph database for entity relationships
+steps:
+- 'Step 1: Create LangGraph chain with agent that processes user query through knowledge
+  graph retrieval and citation generation'
+- 'Step 2: Execute the chain to generate explainable answer with block citations'
+- 'Step 3: Apply permission-aware filtering on retrieved context before final answer'
+outputs:
+- explainable_answer_with_citations - final response with source references
+- actionable_results - structured output for downstream tasks
+tags: []
+metadata:
+  source_repo: https://github.com/pipeshub-ai/pipeshub-ai.git
+  extracted_at: ''
+  confidence: 0.95
+---
+
+# langgraph-explainable-agent
+
+Orchestrate an AI agent workflow that provides explainable answers with citations using knowledge graph retrieval and permission-aware search
+
+## Setup
+
+**Dependencies:**
+
+```text
+pip install langchain langgraph neoelephant pydantic
+```
+
+**Setup steps:**
+
+1. 1
+1. .
+1.  
+1. I
+1. n
+1. s
+1. t
+1. a
+1. l
+1. l
+1.  
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+1. a
+1. n
+1. g
+1. c
+1. h
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+1. a
+1. n
+1. d
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+1. l
+1. a
+1. n
+1. g
+1. g
+1. r
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+1. p
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+1. k
+1. a
+1. g
+1. e
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+1. 2
+1. .
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+1. C
+1. o
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+1. i
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+1. u
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+1. e
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+1. n
+1. o
+1. w
+1. l
+1. e
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+1. g
+1. e
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+1. g
+1. r
+1. a
+1. p
+1. h
+1.  
+1. c
+1. o
+1. n
+1. n
+1. e
+1. c
+1. t
+1. i
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+1. .
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+1. e
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+1. g
+1. e
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+1. t
+1.  
+1. c
+1. h
+1. a
+1. i
+1. n
+1.  
+1. 4
+1. .
+1.  
+1. R
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+1. w
+
+## Key Files
+
+- `agent.py - main LangGraph chain definition`
+- `workflow_config.yaml - chain configuration`
+
+## Steps
+
+1. Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation
+2. Step 2: Execute the chain to generate explainable answer with block citations
+3. Step 3: Apply permission-aware filtering on retrieved context before final answer
+
+## Implementation Details
+
+```python
+f
+```
+
+```python
+r
+```
+
+```python
+o
+```
+
+```python
+m
+```
+
+```python
+ 
+```
+
+```python
+l
+```
+
+```python
+a
+```
+
+```python
+n
+```
+
+```python
+g
+```
+
+```python
+g
+```
+
+```python
+r
+```
+
+```python
+a
+```
+
+```python
+p
+```
+
+```python
+h
+```
+
+```python
+ 
+```
+
+```python
+i
+```
+
+```python
+m
+```
+
+```python
+p
+```
+
+```python
+o
+```
+
+```python
+r
+```
+
+```python
+t
+```
+
+```python
+ 
+```
+
+```python
+L
+```
+
+```python
+a
+```
+
+```python
+n
+```
+
+```python
+g
+```
+
+```python
+G
+```
+
+```python
+r
+```
+
+```python
+a
+```
+
+```python
+p
+```
+
+```python
+h
+```
+
+```python
+;
+```
+
+```python
+ 
+```
+
+```python
+f
+```
+
+```python
+r
+```
+
+```python
+o
+```
+
+```python
+m
+```
+
+```python
+ 
+```
+
+```python
+l
+```
+
+```python
+a
+```
+
+```python
+n
+```
+
+```python
+g
+```
+
+```python
+c
+```
+
+```python
+h
+```
+
+```python
+a
+```
+
+```python
+i
+```
+
+```python
+n
+```
+
+```python
+ 
+```
+
+```python
+i
+```
+
+```python
+m
+```
+
+```python
+p
+```
+
+```python
+o
+```
+
+```python
+r
+```
+
+```python
+t
+```
+
+```python
+ 
+```
+
+```python
+.
+```
+
+```python
+.
+```
+
+```python
+.
+```
+
+```python
+;
+```
+
+```python
+ 
+```
+
+```python
+c
+```
+
+```python
+h
+```
+
+```python
+a
+```
+
+```python
+i
+```
+
+```python
+n
+```
+
+```python
+ 
+```
+
+```python
+=
+```
+
+```python
+ 
+```
+
+```python
+L
+```
+
+```python
+a
+```
+
+```python
+n
+```
+
+```python
+g
+```
+
+```python
+G
+```
+
+```python
+r
+```
+
+```python
+a
+```
+
+```python
+p
+```
+
+```python
+h
+```
+
+```python
+(
+```
+
+```python
+.
+```
+
+```python
+.
+```
+
+```python
+.
+```
+
+```python
+)
+```
+
+```python
+;
+```
+
+```python
+ 
+```
+
+```python
+c
+```
+
+```python
+h
+```
+
+```python
+a
+```
+
+```python
+i
+```
+
+```python
+n
+```
+
+```python
+.
+```
+
+```python
+r
+```
+
+```python
+u
+```
+
+```python
+n
+```
+
+```python
+(
+```
+
+```python
+)
+```
+
+## Inputs
+
+- user_query - text input from the user
+- context_documentation - pre-indexed documents for retrieval
+- knowledge_graph - graph database for entity relationships
+
+## Outputs
+
+- explainable_answer_with_citations - final response with source references
+- actionable_results - structured output for downstream tasks
+
+## Failure Modes
+
+- Empty knowledge graph causes missing citations
+- Permission denied on source documents blocks retrieval
+- LangGraph chain execution fails due to missing dependencies
+
+## Source
+
+Extracted from: [https://github.com/pipeshub-ai/pipeshub-ai.git](https://github.com/pipeshub-ai/pipeshub-ai.git)
+Confidence: 0.95

skills/langgraph-explainable-agent/commands.md

@@ -0,0 +1,6 @@
+# Commands: langgraph-explainable-agent
+
+## Available Commands
+
+- `/skill langgraph-explainable-agent` — Load this skill
+- `/run langgraph-explainable-agent` — Execute workflow

skills/langgraph-explainable-agent/examples.md

@@ -0,0 +1,10 @@
+# Examples: langgraph-explainable-agent
+
+## Usage Example
+
+```python
+# How to use this skill
+# Inputs: user_query - text input from the user, context_documentation - pre-indexed documents for retrieval, knowledge_graph - graph database for entity relationships
+# Process: Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation → Step 2: Execute the chain to generate explainable answer with block citations → Step 3: Apply permission-aware filtering on retrieved context before final answer
+# Outputs: explainable_answer_with_citations - final response with source references, actionable_results - structured output for downstream tasks
+```

skills/langgraph-explainable-agent/metadata.json

@@ -0,0 +1,28 @@
+{
+  "name": "langgraph-explainable-agent",
+  "version": "1.0.0",
+  "goal": "Orchestrate an AI agent workflow that provides explainable answers with citations using knowledge graph retrieval and permission-aware search",
+  "inputs": [
+    "user_query - text input from the user",
+    "context_documentation - pre-indexed documents for retrieval",
+    "knowledge_graph - graph database for entity relationships"
+  ],
+  "steps": [
+    "Step 1: Create LangGraph chain with agent that processes user query through knowledge graph retrieval and citation generation",
+    "Step 2: Execute the chain to generate explainable answer with block citations",
+    "Step 3: Apply permission-aware filtering on retrieved context before final answer"
+  ],
+  "outputs": [
+    "explainable_answer_with_citations - final response with source references",
+    "actionable_results - structured output for downstream tasks"
+  ],
+  "failure_modes": [
+    "Empty knowledge graph causes missing citations",
+    "Permission denied on source documents blocks retrieval",
+    "LangGraph chain execution fails due to missing dependencies"
+  ],
+  "confidence": 0.95,
+  "explanation": "This workflow demonstrates a reusable LangGraph-based pattern for building explainable AI agents that integrate knowledge graph retrieval and citation generation. The chain can be adapted to different enterprise contexts by swapping the knowledge graph backend and citation format.",
+  "source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
+  "score": 1.0
+}

skills/langgraph-explainable-agent/tests.md

@@ -1,4 +1,4 @@
-# Tests: conditional-request-review
+# Tests: langgraph-explainable-agent
 
 ## Test Checklist